Foreword
Foreword
Preface
Competing Interests
Acknowledgments
Contents
List of Contributors
Editors
Associate Editors
Authors
Reviewers
List of Abbreviations
Capturing the Wealth and Diversity of Learning Processes with Learning Analytics Methods
1 Introduction
2 How the Book Is Structured
2.1 Introductory Chapters
2.2 Machine Learning Methods
2.3 Temporal Methods
2.4 Network Analysis
2.5 Psychometrics
3 The Companion Code and Data
References
Part I Getting Started
A Broad Collection of Datasets for Educational Research Training and Application
1 Introduction
2 Types of Data
2.1 Contextual Data
2.2 Self-reported Data
2.3 Activity Data
2.4 Social Interaction Data
2.5 Performance Data
2.6 Other Types of Data
3 Dataset Selection
3.1 LMS Data from a Blended Course on Learning Analytics
3.1.1 Events
3.1.2 Demographics
3.1.3 Results
3.1.4 AllCombined
3.2 LMS Data from a Higher Education Institution in Oman
3.2.1 Student Academic Information
3.2.2 Moodle
3.2.3 Activity
3.2.4 Results
3.2.5 eDify
3.3 School Engagement, Academic Achievement, and Self-regulated Learning
3.4 Teacher Burnout Survey Data
3.5 Interdisciplinary Academic Writing Self-efficacy
3.6 Educators' Discussions in a MOOC (SNA)
3.7 High School Learners' Interactions (SNA)
3.8 Interactions in an LMS Forum from a Programming Course (SNA)
3.9 Engagement and Achievement Throughout a Study Program
3.9.1 Longitudinal Engagement Indicators and Grades
3.9.2 Longitudinal Engagement and Achievement States
3.10 University Students' Basic Need Satisfaction, Self-regulated Learning and Well-Being During COVID-19
4 Discussion
References
Getting Started with R for Education Research
1 Introduction
2 Learning R
3 RStudio
4 Best Practices in Programming
4.1 R Markdown
4.2 How Is Code Developed?
5 Basic Operations
5.1 Arithmetic Operators
5.2 Relational Operators
5.3 Logical Operators
5.4 Special Operators
6 Basic Data Types and Variables
7 Basic R Objects
8 Working with Dataframes
8.1 tibble
9 Pipes
9.1 magrittr pipe %>%
9.2 Native pipe |>
10 Lists
11 Functions
12 Conditional Statements
13 Looping Constructs
14 Discussion and Other Resources for Learning R
References
An R Approach to Data Cleaning and Wrangling for Education Research
1 Introduction
2 Reading Data into R
3 Grouping and Summarizing Data
4 Selecting Variables
5 Filtering Observations
6 Transforming Variables
7 Rearranging Data
8 Reshaping Data
9 Joining Data
10 Missing Data
11 Correcting Erroneous Data
12 Conclusion and Further Reading
References
Introductory Statistics with R for Educational Researchers
1 Introduction
2 Descriptive Statistics
2.1 Measures of Central Tendency
2.2 Measures of Dispersion
2.3 Covariance and Correlation
2.4 Other Common Statistics
3 Statistical Hypothesis Testing
3.1 Student's t-test
3.1.1 One-Sample t-test
3.1.2 Two-Sample t-test
3.1.3 Paired Two-Sample t-test
3.2 Chi-Squared Test
3.3 Analysis of Variance
3.4 Levene's Test
3.5 Shapiro-Wilk Test
4 Correlation
5 Linear Regression
6 Logistic Regression
7 Conclusion
8 Further Reading
References
Visualizing and Reporting Educational Data with R
1 Introduction
2 Visualization in Learning Analytics
3 Generating plots with ggplot2
3.1 The ggplot2 grammar
3.2 Creating Your First Plot
3.2.1 Installing ggplot2
3.2.2 Downloading the Data
3.2.3 Creating the Aesthetic Mapping
3.2.4 Add the Geometry Component
3.2.5 Adding the Color Scale
3.2.6 Working with Themes
3.2.7 Changing the Axis Ticks
3.2.8 Titles and Labels
3.2.9 Other Cosmetic Modifications
3.2.10 Saving the Plot
3.3 Types of Plots
3.3.1 Bar Plot
3.3.2 Histogram
3.3.3 Line Plot
3.3.4 Jitter Plots
3.3.5 Box Plot
3.3.6 Violin Plot
3.3.7 Scatter Plots
3.4 Advanced Features
3.4.1 Plot Grids
3.4.2 Combining Multiple Plots
4 Creating Tables with gt
5 Discussion
6 Additional Material
References
Part II Machine Learning
Predictive Modelling in Learning Analytics: A Machine Learning Approach in R
1 Introduction
2 Predictive Modelling: Objectives, Features, and Algorithms
3 Predicting Students' Course Success Early in the Course
3.1 Prediction Objectives and Methods
3.2 Context
3.3 An Overview of the Required Tools (R Packages)
3.4 Data Preparation and Exploration
3.5 Feature Engineering
3.6 Predicting Success Category
3.7 Predicting Success Score
4 Concluding Remarks
5 Suggested Readings
References
Dissimilarity-Based Cluster Analysis of Educational Data: A Comparative Tutorial Using R
1 Introduction
2 Clustering in Education: Review of the Literature
3 Clustering Methodology
3.1 K-Means
3.1.1 K-Means Algorithm
3.1.2 K-means Limitations and Practical Concerns
3.2 Agglomerative Hierarchical Clustering
3.2.1 Linkage Criteria
3.2.2 Cutting the Dendrogram
3.3 Choosing the Number of Clusters
4 Tutorial with R
4.1 The Data Set
4.1.1 Pre-processing the Data
4.2 Clustering Applications
4.2.1 K-means Application
4.2.2 K-medoids Application
4.2.3 Agglomerative Hierarchical Clustering Application
4.2.4 Identifying the Optimal Clustering Solution
4.2.5 Interpreting the Optimal Clustering Solution
5 Discussion and Further Readings
References
An Introduction and R Tutorial to Model-Based Clustering in Education via Latent Profile Analysis
1 Introduction
2 Literature Review
3 Model-Based Clustering
3.1 Latent Variable Models
3.2 Finite Gaussian Mixture Models
4 Gaussian Parsimonious Clustering Models
4.1 Model Selection
4.2 mclust R Package
4.3 Other Practical Issues and Extensions
4.3.1 Bayesian Regularisation
4.3.2 Bootstrap Inference
4.3.3 Entropy and Average Posterior Probabilities
5 Application: School Engagement, Academic Achievement, and Self-regulated Learning
5.1 Preparing the Data
5.2 Model Estimation and Model Selection
5.3 Examining Model Output
6 Discussion
References
Part III Temporal Methods
Sequence Analysis in Education: Principles, Technique, and Tutorial with R
1 Introduction
2 Review of the Literature
3 Basics of Sequences
3.1 Steps of Sequence Analysis
3.1.1 The Alphabet
3.1.2 Specifying the Time Scheme
3.1.3 Defining the Actor
3.1.4 Building the Sequences
3.1.5 Visualizing and Exploring the Sequence Data
3.1.6 Calculating the Dissimilarities Between Sequences
3.1.7 Finding Similar Groups or Clusters of Sequences
3.1.8 Analyzing the Groups and/or Using Them in Subsequent Analyses
3.2 Introduction to the Technique
3.3 Sequence Visualization
4 Analysis of the Data with Sequence Mining in R
4.1 Important Packages
4.2 Reading the Data
4.3 Preparing the Data for Sequence Analysis
4.4 Statistical Properties of the Sequences
4.5 Visualizing Sequences
4.6 Dissimilarity Analysis and Clustering
5 More Resources
References
Modeling the Dynamics of Longitudinal Processes in Education. A Tutorial with R for the VaSSTra Method
1 Introduction
2 VaSSTra: From Variables to States, from States to Sequences, from Sequences to Trajectories
3 Review of the Literature
4 VassTra with R
4.1 The Packages
4.2 The Dataset
4.3 From Variables to States
4.4 From States to Sequences
4.5 From Sequences to Trajectories
4.6 Studying Trajectories
5 Discussion
References
A Modern Approach to Transition Analysis and Process Mining with Markov Models in Education
1 Introduction
2 Methodological Background
2.1 Markov Model
2.2 Mixture Markov Model
2.3 Hidden Markov Model
2.4 Mixture Hidden Markov Models
2.5 Multi-Channel Sequences
2.6 Estimating Model Parameters
3 Review of the Literature
4 Examples
4.1 Steps of Estimation
4.1.1 Defining the Model Structure
4.1.2 Estimating the Model Parameters
4.1.3 Examining the Results
4.2 Markov Models
4.2.1 Markov Model
4.2.2 Hidden Markov Models
4.2.3 Mixture Markov Models
4.2.4 Mixture Hidden Markov Models
4.3 Stochastic Process Mining with Markovian Models
5 Conclusions and Further Readings
References
Multi-Channel Sequence Analysis in Educational Research: An Introduction and Tutorial with R
1 Introduction
2 Multi-Channel Sequence Analysis
2.1 Step 1: Building the Channel Sequences
2.2 Step 2: Visualising the Multi-Channel Sequence
2.3 Step 3: Finding Patterns (Clusters or Trajectories)
2.3.1 Traditional Sequence Analysis Extensions
2.3.2 Mixture Hidden Markov Models
2.4 Step 4: Relating Clusters to Covariates
3 Review of the Literature
4 Case Study: The Longitudinal Association of Engagement and Achievement
4.1 The Packages
4.2 The Data
4.3 Creating the Sequences
4.3.1 Engagement Channel
4.3.2 Achievement Channel
4.3.3 Visualising the Multi-Channel Sequence
4.4 Clustering via Multi-Channel Dissimilarities
4.5 Building a Mixture Hidden Markov Model
4.6 Incorporating Covariates in MHMMs
5 Discussion
6 Further Readings
References
The Why, the How and the When of Educational Process Mining in R
1 Introduction
2 Basic Steps in Process Mining
3 Review of the Literature
4 Process Mining with R
4.1 The Libraries
4.2 Importing the Data
4.2.1 Creating an Event Log
4.2.2 Inspecting the Logs
4.2.3 Visualizing the Process
5 Discussion
6 Further Readings
References
Part IV Network Analysis
Social Network Analysis: A Primer, a Guide and a Tutorial in R
1 Introduction
1.1 What Are Networks?
2 Analysis of Social Networks
2.1 Mathematical Analysis
2.1.1 Graph-Level Measures
2.1.2 Local Centrality Measures
2.1.3 Measures Based on Shortest Paths
2.1.4 Eigenvector-Based Centralities
2.1.5 Other Measures
2.2 Network Visualization
2.3 Network Analysis
3 Network Analysis in R
3.1 Graph Level Analysis
3.2 Network Connectivity
3.3 Network Operations
3.4 Individual Vertex Measures (Centrality Measures)
4 Discussion
5 More Reading Resources
References
Community Detection in Learning Networks Using R
1 Introduction to Community Detection in Social Networks
2 Community Detection in Social Networks Based on Educational Data
3 Algorithms for Community Detection
4 Community Detection in R: An Annotated Example Using igraph
4.1 Interactive Visualization of Communities in R
4.1.1 visNetwork
4.1.2 networkD3
5 Concluding Notes
6 Further Readings
References
Temporal Network Analysis: Introduction, Methods and Analysis with R
1 Introduction
2 The Building Blocks of a Temporal Network
2.1 Edges
2.2 Paths, Concurrency, and Reachability
2.3 Nodes
3 Previous Work and Examples of Temporal Network Analysis
4 Tutorial: Building a Temporal Network
4.1 Visualization of Temporal Networks
4.2 Statistical Analysis of Temporal Networks
4.2.1 Graph Level Measures
4.2.2 Node-Level Measures (Temopral Centrality Measures)
5 Discussion
6 Learning Resources
References
Epistemic Network Analysis and Ordered Network Analysis in Learning Analytics
1 Introduction
2 Literature Review
2.1 Epistemic Network Analysis (ENA)
2.2 Ordered Network Analysis (ONA)
3 Epistemic Network Analysis in R
3.1 Install the rENA Package and Load the Library
3.2 Dataset
3.3 Construct an ENA Model
3.3.1 Specify Units
3.3.2 Specify Codes
3.3.3 Specify Conversations
3.3.4 Specify the Window
3.3.5 Specify Groups and Rotation Method
3.3.6 Specify Metadata
3.3.7 Construct an Model
3.4 Summary of Key Model Outputs
3.4.1 Connection Counts
3.4.2 Line Weights
3.4.3 ENA Points
3.4.4 Rotation Matrix
3.4.5 Metadata
3.5 ENA Visualization
3.5.1 Plot a Mean Network
3.5.2 Plot a Mean Network and its Points
3.5.3 Plot an Individual Unit Network and its Point
3.5.4 Plot Everything, Everywhere, All at Once
3.6 Compare Groups Statistically
3.7 Model Evaluation
3.7.1 Variance Explained
3.7.2 Goodness of Fit
3.7.3 Close the Interpretative Loop
3.8 Using ENA Model Outputs in Other Analyses
4 Ordered Network Analysis with R
4.1 Install the ONA Package and Load the Library
4.2 Dataset
4.3 Construct an ONA Model
4.3.1 Specify Units
4.3.2 Specify Codes
4.3.3 Specify Conversations
4.3.4 Specify the Window
4.3.5 Specify Metadata
4.3.6 Accumulate Connections
4.3.7 Construct an ONA Model
4.4 Summary of Key Model Outputs
4.4.1 Connection Counts
4.4.2 Line Weights
4.4.3 ONA Points
4.4.4 Rotation Matrix
4.4.5 Metadata
4.5 ONA Visualization
4.5.1 Plot a Mean Network
4.5.2 Plot a Mean Network and its Points
4.5.3 Plot an Individual Network and its Points
4.6 Compare Groups Statistically
4.7 Model Evaluation
4.7.1 Variance Explained
4.7.2 Goodness of Fit
4.7.3 Close the Interpretative Loop
4.8 Using ONA Model Outputs in Other Analyses
5 Additional Features
5.1 Projections in ENA
5.2 Projections in ONA
6 Discussion
References
Further Reading
Part V Psychometrics
Psychological Networks: A Modern Approach to Analysis of Learning and Complex Learning Processes
1 Introduction
1.1 Complex Systems
1.2 Network Analysis
2 Related Work
3 Tutorial with R
3.1 The Libraries
3.2 Importing and Preparing the Data
3.3 Assumption Checks
3.4 Network Estimation
3.5 Plotting the Network
3.6 Explaining Network Relationships
3.7 Network Inference
3.8 Comparing Networks
3.9 The Variability Network
3.10 Evaluation of Robustness and Accuracy
3.11 Discussion
References
Factor Analysis in Education Research Using R
1 Introduction
2 Literature Review
3 Recap of the Factor Analysis Model
4 Integrated Strategy for a Factor Analysis
4.1 Step 1: Exploring the Factor Structure
4.2 Step 2: Building the Factor Model and Assessing Fit
4.3 Step 3: Assessing Generalizability
5 Factor Analysis in R
5.1 Preparation
5.1.1 Reading in the Data
5.1.2 Are the Data Suited for Factor Analysis?
5.1.3 Setting a Holdout Sample Apart
5.2 Step 1: Exploring the Factor Structure
5.3 Step 2: Building the Factor Model and Assessing Fit
5.4 Step 3: Assessing Generalizability
6 Conclusion
7 Further Readings
References
Structural Equation Modeling with R for Education Scientists
1 Introduction
2 Literature Review
3 Recap of SEM
4 Integrated Strategy for Structural Equation Modeling
4.1 Step 1: Steps from the Previous Chapter While Assessing Configural Invariance
4.2 Step 2: Assessing Higher Levels of Invariance
4.3 Step 3: Building the Structural Equation Model and Assessing Fit
5 SEM in R
5.1 Preparation
5.1.1 Reading in the Data
5.1.2 Are the Data Suited for SEM?
5.2 Step 1: Steps from the Previous Chapter
5.3 Step 2: Assessing Higher Levels of Invariance
5.4 Step 3: Building the Structural Equation Model and Assessing Fit
6 Conclusion
7 Further Readings
References
Why Educational Research Needs a Complex System Revolution that Embraces Individual Differences, Heterogeneity, and Uncertainty
1 Introduction
2 Complex Systems and Education
2.1 Dynamics in Complex Systems
2.2 From Theory to Practice: Measurement and Analyses
2.3 Complex Systems and Individual Differences
2.3.1 The Individual
2.3.2 Heterogeneity
3 Conclusion
References
Index